MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation

Knowledge-Based Systems

Volumn 89, Issue , 2015, Pages 385-397

  Charte, Francisco ^a   Rivera, Antonio J ^b   Del Jesus, María J ^b   Herrera, Francisco ^a,c  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF JAÉN   (Spain)

^c KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Imbalanced learning; Multilabel classification; Oversampling; Synthetic instance generation

Indexed keywords

ARTIFICIAL INTELLIGENCE; KNOWLEDGE BASED SYSTEMS; SOFTWARE ENGINEERING;

CLASSIFICATION RESULTS; IMBALANCED LEARNING; MULTI-LABEL CLASSIFICATIONS; MULTI-LABEL LEARNING; OVER SAMPLING; REAL-WORLD SCENARIO; SYNTHETIC INSTANCE GENERATION; SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUES;

CLASSIFICATION (OF INFORMATION);

EID: 84944354565     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2015.07.019     Document Type: Article

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